It is known to embody mixture metering systems for internal combustion engines in such a way that fuel metering is effected via so-called learning, or adaptive, closed-loop control or regulating systems. In this connection, reference can be made to U.S. Pats. No. 4,487,186 and 4,715,344. A learning regulating system of this kind has values for the injection, for example stored in a characteristic field, and these values can then be copied into a read-write memory each time the engine is started. By means of the characteristic fields, fast-reacting pilot or anticipatory control for the injection, for example, or generally for fuel metering or other quantities that must be adapted as quickly as possible to varying operating conditions of an internal combustion engine, such as the instant of ignition, the rate of exhaust gas recirculation and the like, are obtained. To achieve learning regulating systems here, the various characteristic field values can be corrected as a function of operating characteristics and then written into the appropriate memory.
In connection with the above, U.S. Pat. No. 4,676,215 and U.S. Pat. No. 4,827,937 can be consulted and both applications are incorporated herein by reference. These applications also relate to the possibility, in the generic methods and apparatus, of varying values stored in a characteristic field and addressed as a function of engine operating characteristic quantities in accordance with a learning process in such a way that not only merely a single predetermined characteristic field value, but the various characteristic field values located in its vicinity as well, can be additionally modified so as to vary the particular characteristic field value in question. The procedure, in more, detail, is such (as described in the above-mentioned U.S. Pat. No. 4,676,215) that an integral regulator varies the value read out of the characteristic field continuously in a multiplicative manner during actual operation of the engine, but at the same time the multiplicative correction factor RF of the regulator is averaged and, upon leaving the influenced surrounding region or inclusion area of a particular support point, is incorporated as a mean value into the corresponding support point of the characteristic field. The characteristic field is divided up into a predetermined number of support points, so that intermediate values can be calculated by means of linear interpolation. In this manner it is possible on the one hand to adapt the characteristic field to the values predetermined by the regulator by means of varying the support points, and on the other hand to avoid the situation where only the addressed values of the characteristic field are capable of learning, which would be the case if there were only single-value adaptation.
In this connection, it is proposed in U.S. Pat. No. 4,827,937, that the disturbing quantities that make up the majority of the characteristic field changes and that operate multiplicatively be detected by the introduction of a so-called global factor and superimposed on the entire characteristic field, so that the field can be adapted substantially faster. As a result, areas in the characteristic field that are addressed only rarely, or very rarely, are adapted faster and correspondingly more accurately as well. It is also possible in such a system, by subdividing it into a basic characteristic field and a factor characteristic field that performs the self-adaptation (adaptive learning), to assure that the interpolation that is to be performed in the area of the basic characteristic field cannot have any disruptive effect on the learning process. The self-adapting factor characteristic field then serves primarily to take additive or structural influences and disturbing quantities into account, while multiplicative quantities, which typically make up a uniform proportion of the disturbing quantities, can be detected by means of a combination with the above-mentioned global factor, so that overall, it is possible to attain fast and optimal adaptation while taking structural and multiplicative influences into account.
However, it has been found that further improvements, in particular with respect to the transient phenomena, of the course of adaptation are still possible, especially in structural changes and in the parameter sensitivity.